arXiv AI
Real-TurnTurk: A Multimodal Turkish Corpus for Turn-Taking Prediction
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arXiv AI
Jul 3
TurnNat: Automatic Evaluation of Turn-Taking Naturalness in Dyadic Spoken Dialogue
arXiv:2607. 01345v1 Announce Type: cross Abstract: Turn-taking naturalness is central to full-duplex spoken dialogue systems, yet its automatic evaluation remains limited.
arXiv Computation and Language
Aug 27
TurnBench: A Multi-Domain Benchmark for Turn-Taking Dynamics in Spoken Dialogue
TurnBench is a new multi‑domain benchmark for evaluating turn‑taking dynamics in spoken dialogue. It comprises a 30‑hour hand‑labeled corpus of dyadic human conversations, a standardized evaluation protocol for end‑of‑turn and interruption detection, and covers six distinct interaction styles with triple annotation. The benchmark also provides a 104‑hour training set, a public leaderboard, and an interactive dataset viewer at https://turnbench.sesame.com.
arXiv AI
Aug 19
Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges
The article surveys multi‑turn conversational AI, highlighting its shift from isolated text prompts to sustained, multimodal interactions that involve clarifying goals, revising requests, and switching topics. It reviews literature across text‑only dialogue, AudioLLMs, multimodal and omni‑modal systems, and tool‑augmented agents, organizing findings around datasets, models, training, evaluation, and cross‑cutting challenges. The analysis reveals that while multimodal perception and action have progressed rapidly, systems still struggle with persistent memory, cross‑turn grounding, full‑duplex interaction, robust evaluation, and cultural alignment.
arXiv Computation and Language
3d ago
DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues
arXiv:2607.26178v2 Announce Type: replace Abstract: Turn-taking is a central component of full-duplex interaction. Which turn-taking behaviors are appropriate varies with the scenario, yet current mo...
arXiv AI
Jun 16
MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild
arXiv:2606. 16731v1 Announce Type: cross Abstract: Current multiparty turn-taking models often rely on complex microphone arrays or multi-camera setups, limiting their applicability in human-robot interaction scenarios.
arXiv Computation and Language
4d ago
Predicting Turn-Taking Outcomes in Multi-Party Conversation: Interpretable Modelling of Speech and Gaze Dynamics with Interpersonal Closeness
The study investigates how gaze and speech cues, together with perceived interpersonal closeness, predict turn‑taking outcomes in free four‑person conversations. Using the GaMMA corpus, logistic regression models were trained on interpretable features such as gaze transition motifs, entropy, addressee identity, mutual gaze, and speaker loudness to classify floor‑transfer events as gaps or overlaps. Results show that gaze features alone capture predictive structure, and combining them with loudness yields a robust classifier (ROC AUC = 0.76 ± 0.04) that remains effective even under noisy conditions.